Overview

Dataset statistics

Number of variables37
Number of observations2368
Missing cells0
Missing cells (%)0.0%
Total size in memory518.1 KiB
Average record size in memory224.1 B

Variable types

Numeric18
Text19

Alerts

Unnamed: 0 has unique valuesUnique
key_id has unique valuesUnique
group_stage has 620 (26.2%) zerosZeros
knockout_stage has 1748 (73.8%) zerosZeros
replayed has 2360 (99.7%) zerosZeros
replay has 2360 (99.7%) zerosZeros
home_team has 1184 (50.0%) zerosZeros
away_team has 1184 (50.0%) zerosZeros
goals_for has 677 (28.6%) zerosZeros
goals_against has 677 (28.6%) zerosZeros
goal_differential has 476 (20.1%) zerosZeros
extra_time has 2202 (93.0%) zerosZeros
penalty_shootout has 2292 (96.8%) zerosZeros
penalties_for has 2293 (96.8%) zerosZeros
penalties_against has 2293 (96.8%) zerosZeros
win has 1384 (58.4%) zerosZeros
lose has 1384 (58.4%) zerosZeros
draw has 1968 (83.1%) zerosZeros

Reproduction

Analysis started2023-10-23 21:40:20.717106
Analysis finished2023-10-23 21:40:21.457880
Duration0.74 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct2368
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1183.5
Minimum0
Maximum2367
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:21.584749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.35
Q1591.75
median1183.5
Q31775.25
95-th percentile2248.65
Maximum2367
Range2367
Interquartile range (IQR)1183.5

Descriptive statistics

Standard deviation683.7270411
Coefficient of variation (CV)0.577716131
Kurtosis-1.2
Mean1183.5
Median Absolute Deviation (MAD)592
Skewness0
Sum2802528
Variance467482.6667
MonotonicityStrictly increasing
2023-10-23T23:40:21.821087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1573 1
 
< 0.1%
1575 1
 
< 0.1%
1576 1
 
< 0.1%
1577 1
 
< 0.1%
1578 1
 
< 0.1%
1579 1
 
< 0.1%
1580 1
 
< 0.1%
1581 1
 
< 0.1%
1582 1
 
< 0.1%
Other values (2358) 2358
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
ValueCountFrequency (%)
2367 1
< 0.1%
2366 1
< 0.1%
2365 1
< 0.1%
2364 1
< 0.1%
2363 1
< 0.1%

key_id
Real number (ℝ)

UNIQUE 

Distinct2368
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1184.5
Minimum1
Maximum2368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:22.044884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile119.35
Q1592.75
median1184.5
Q31776.25
95-th percentile2249.65
Maximum2368
Range2367
Interquartile range (IQR)1183.5

Descriptive statistics

Standard deviation683.7270411
Coefficient of variation (CV)0.5772284011
Kurtosis-1.2
Mean1184.5
Median Absolute Deviation (MAD)592
Skewness0
Sum2804896
Variance467482.6667
MonotonicityStrictly increasing
2023-10-23T23:40:22.287606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1574 1
 
< 0.1%
1576 1
 
< 0.1%
1577 1
 
< 0.1%
1578 1
 
< 0.1%
1579 1
 
< 0.1%
1580 1
 
< 0.1%
1581 1
 
< 0.1%
1582 1
 
< 0.1%
1583 1
 
< 0.1%
Other values (2358) 2358
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
ValueCountFrequency (%)
2368 1
< 0.1%
2367 1
< 0.1%
2366 1
< 0.1%
2365 1
< 0.1%
2364 1
< 0.1%

tournament_id
Text

Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:22.504264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters16576
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWC-1930
2nd rowWC-1930
3rd rowWC-1930
4th rowWC-1930
5th rowWC-1930
ValueCountFrequency (%)
wc-2006 128
 
5.4%
wc-2018 128
 
5.4%
wc-2002 128
 
5.4%
wc-2014 128
 
5.4%
wc-1998 128
 
5.4%
wc-2010 128
 
5.4%
wc-1986 104
 
4.4%
wc-1994 104
 
4.4%
wc-1990 104
 
4.4%
wc-2019 104
 
4.4%
Other values (19) 1184
50.0%
2023-10-23T23:40:22.950083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 2368
14.3%
C 2368
14.3%
- 2368
14.3%
1 2100
12.7%
9 2000
12.1%
0 1800
10.9%
2 1336
8.1%
8 646
 
3.9%
6 424
 
2.6%
4 394
 
2.4%
Other values (3) 772
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9472
57.1%
Uppercase Letter 4736
28.6%
Dash Punctuation 2368
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2100
22.2%
9 2000
21.1%
0 1800
19.0%
2 1336
14.1%
8 646
 
6.8%
6 424
 
4.5%
4 394
 
4.2%
5 322
 
3.4%
7 280
 
3.0%
3 170
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
W 2368
50.0%
C 2368
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11840
71.4%
Latin 4736
 
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2368
20.0%
1 2100
17.7%
9 2000
16.9%
0 1800
15.2%
2 1336
11.3%
8 646
 
5.5%
6 424
 
3.6%
4 394
 
3.3%
5 322
 
2.7%
7 280
 
2.4%
Latin
ValueCountFrequency (%)
W 2368
50.0%
C 2368
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 2368
14.3%
C 2368
14.3%
- 2368
14.3%
1 2100
12.7%
9 2000
12.1%
0 1800
10.9%
2 1336
8.1%
8 646
 
3.9%
6 424
 
2.6%
4 394
 
2.4%
Other values (3) 772
 
4.7%

tournament_name
Text

Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:23.173813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length27
Median length25
Mean length25.47972973
Min length25

Characters and Unicode

Total characters60336
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1930 FIFA Men's World Cup
2nd row1930 FIFA Men's World Cup
3rd row1930 FIFA Men's World Cup
4th row1930 FIFA Men's World Cup
5th row1930 FIFA Men's World Cup
ValueCountFrequency (%)
fifa 2368
20.0%
world 2368
20.0%
cup 2368
20.0%
men's 1800
15.2%
women's 568
 
4.8%
2014 128
 
1.1%
1998 128
 
1.1%
2006 128
 
1.1%
2002 128
 
1.1%
2018 128
 
1.1%
Other values (24) 1728
14.6%
2023-10-23T23:40:23.621868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9472
 
15.7%
F 4736
 
7.8%
o 2936
 
4.9%
W 2936
 
4.9%
r 2368
 
3.9%
s 2368
 
3.9%
u 2368
 
3.9%
C 2368
 
3.9%
d 2368
 
3.9%
l 2368
 
3.9%
Other values (18) 26048
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22448
37.2%
Uppercase Letter 16576
27.5%
Space Separator 9472
15.7%
Decimal Number 9472
15.7%
Other Punctuation 2368
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2936
13.1%
r 2368
10.5%
s 2368
10.5%
u 2368
10.5%
d 2368
10.5%
l 2368
10.5%
n 2368
10.5%
e 2368
10.5%
p 2368
10.5%
m 568
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 2100
22.2%
9 2000
21.1%
0 1800
19.0%
2 1336
14.1%
8 646
 
6.8%
6 424
 
4.5%
4 394
 
4.2%
5 322
 
3.4%
7 280
 
3.0%
3 170
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
F 4736
28.6%
W 2936
17.7%
C 2368
14.3%
A 2368
14.3%
I 2368
14.3%
M 1800
 
10.9%
Space Separator
ValueCountFrequency (%)
9472
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39024
64.7%
Common 21312
35.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 4736
 
12.1%
o 2936
 
7.5%
W 2936
 
7.5%
r 2368
 
6.1%
s 2368
 
6.1%
u 2368
 
6.1%
C 2368
 
6.1%
d 2368
 
6.1%
l 2368
 
6.1%
n 2368
 
6.1%
Other values (6) 11840
30.3%
Common
ValueCountFrequency (%)
9472
44.4%
' 2368
 
11.1%
1 2100
 
9.9%
9 2000
 
9.4%
0 1800
 
8.4%
2 1336
 
6.3%
8 646
 
3.0%
6 424
 
2.0%
4 394
 
1.8%
5 322
 
1.5%
Other values (2) 450
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9472
 
15.7%
F 4736
 
7.8%
o 2936
 
4.9%
W 2936
 
4.9%
r 2368
 
3.9%
s 2368
 
3.9%
u 2368
 
3.9%
C 2368
 
3.9%
d 2368
 
3.9%
l 2368
 
3.9%
Other values (18) 26048
43.2%

match_id
Text

Distinct1184
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:23.982815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters21312
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM-1930-01
2nd rowM-1930-01
3rd rowM-1930-02
4th rowM-1930-02
5th rowM-1930-03
ValueCountFrequency (%)
m-1930-01 2
 
0.1%
m-1930-18 2
 
0.1%
m-1930-04 2
 
0.1%
m-1930-05 2
 
0.1%
m-1930-06 2
 
0.1%
m-1930-07 2
 
0.1%
m-1930-08 2
 
0.1%
m-1930-09 2
 
0.1%
m-1930-10 2
 
0.1%
m-1930-11 2
 
0.1%
Other values (1174) 2348
99.2%
2023-10-23T23:40:24.505163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4736
22.2%
1 2944
13.8%
0 2536
11.9%
M 2368
11.1%
9 2214
10.4%
2 2096
9.8%
4 878
 
4.1%
8 868
 
4.1%
3 744
 
3.5%
6 714
 
3.4%
Other values (2) 1214
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14208
66.7%
Dash Punctuation 4736
 
22.2%
Uppercase Letter 2368
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2944
20.7%
0 2536
17.8%
9 2214
15.6%
2 2096
14.8%
4 878
 
6.2%
8 868
 
6.1%
3 744
 
5.2%
6 714
 
5.0%
5 710
 
5.0%
7 504
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 4736
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18944
88.9%
Latin 2368
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4736
25.0%
1 2944
15.5%
0 2536
13.4%
9 2214
11.7%
2 2096
11.1%
4 878
 
4.6%
8 868
 
4.6%
3 744
 
3.9%
6 714
 
3.8%
5 710
 
3.7%
Latin
ValueCountFrequency (%)
M 2368
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4736
22.2%
1 2944
13.8%
0 2536
11.9%
M 2368
11.1%
9 2214
10.4%
2 2096
9.8%
4 878
 
4.1%
8 868
 
4.1%
3 744
 
3.5%
6 714
 
3.4%
Other values (2) 1214
 
5.7%

match_name
Text

Distinct865
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:24.826514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length36
Median length32
Mean length19.66891892
Min length12

Characters and Unicode

Total characters46576
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance vs Mexico
2nd rowFrance vs Mexico
3rd rowUnited States vs Belgium
4th rowUnited States vs Belgium
5th rowYugoslavia vs Brazil
ValueCountFrequency (%)
vs 2368
29.9%
germany 318
 
4.0%
brazil 286
 
3.6%
italy 190
 
2.4%
england 190
 
2.4%
sweden 182
 
2.3%
argentina 180
 
2.3%
united 172
 
2.2%
france 170
 
2.1%
states 166
 
2.1%
Other values (95) 3700
46.7%
2023-10-23T23:40:25.426805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5554
 
11.9%
a 5070
 
10.9%
s 3310
 
7.1%
e 3098
 
6.7%
n 3018
 
6.5%
r 2662
 
5.7%
v 2638
 
5.7%
i 2498
 
5.4%
t 1868
 
4.0%
l 1816
 
3.9%
Other values (37) 15044
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35512
76.2%
Space Separator 5554
 
11.9%
Uppercase Letter 5510
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5070
14.3%
s 3310
9.3%
e 3098
 
8.7%
n 3018
 
8.5%
r 2662
 
7.5%
v 2638
 
7.4%
i 2498
 
7.0%
t 1868
 
5.3%
l 1816
 
5.1%
o 1720
 
4.8%
Other values (15) 7814
22.0%
Uppercase Letter
ValueCountFrequency (%)
S 894
16.2%
C 516
9.4%
B 452
 
8.2%
N 420
 
7.6%
A 416
 
7.5%
G 386
 
7.0%
U 356
 
6.5%
I 316
 
5.7%
E 268
 
4.9%
P 224
 
4.1%
Other values (11) 1262
22.9%
Space Separator
ValueCountFrequency (%)
5554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41022
88.1%
Common 5554
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5070
 
12.4%
s 3310
 
8.1%
e 3098
 
7.6%
n 3018
 
7.4%
r 2662
 
6.5%
v 2638
 
6.4%
i 2498
 
6.1%
t 1868
 
4.6%
l 1816
 
4.4%
o 1720
 
4.2%
Other values (36) 13324
32.5%
Common
ValueCountFrequency (%)
5554
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5554
 
11.9%
a 5070
 
10.9%
s 3310
 
7.1%
e 3098
 
6.7%
n 3018
 
6.5%
r 2662
 
5.7%
v 2638
 
5.7%
i 2498
 
5.4%
t 1868
 
4.0%
l 1816
 
3.9%
Other values (37) 15044
32.3%

stage_name
Text

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:25.640548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length11
Mean length11.41554054
Min length5

Characters and Unicode

Total characters27032
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgroup stage
2nd rowgroup stage
3rd rowgroup stage
4th rowgroup stage
5th rowgroup stage
ValueCountFrequency (%)
group 1736
37.2%
stage 1736
37.2%
round 222
 
4.8%
of 210
 
4.5%
16 210
 
4.5%
quarter-finals 132
 
2.8%
semi-finals 72
 
1.5%
second 72
 
1.5%
final 68
 
1.5%
quarter-final 64
 
1.4%
Other values (3) 140
 
3.0%
2023-10-23T23:40:26.073599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 3472
12.8%
a 2408
8.9%
r 2404
8.9%
2294
8.5%
o 2240
8.3%
e 2162
8.0%
u 2154
8.0%
s 2116
7.8%
t 2040
7.5%
p 1790
6.6%
Other values (12) 3952
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23964
88.7%
Space Separator 2294
 
8.5%
Decimal Number 420
 
1.6%
Dash Punctuation 354
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 3472
14.5%
a 2408
10.0%
r 2404
10.0%
o 2240
9.3%
e 2162
9.0%
u 2154
9.0%
s 2116
8.8%
t 2040
8.5%
p 1790
7.5%
n 662
 
2.8%
Other values (8) 2516
10.5%
Decimal Number
ValueCountFrequency (%)
6 210
50.0%
1 210
50.0%
Space Separator
ValueCountFrequency (%)
2294
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23964
88.7%
Common 3068
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 3472
14.5%
a 2408
10.0%
r 2404
10.0%
o 2240
9.3%
e 2162
9.0%
u 2154
9.0%
s 2116
8.8%
t 2040
8.5%
p 1790
7.5%
n 662
 
2.8%
Other values (8) 2516
10.5%
Common
ValueCountFrequency (%)
2294
74.8%
- 354
 
11.5%
6 210
 
6.8%
1 210
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 3472
12.8%
a 2408
8.9%
r 2404
8.9%
2294
8.5%
o 2240
8.3%
e 2162
8.0%
u 2154
8.0%
s 2116
7.8%
t 2040
7.5%
p 1790
6.6%
Other values (12) 3952
14.6%

group_name
Text

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:26.252358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length14
Median length7
Mean length8.868243243
Min length7

Characters and Unicode

Total characters21000
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGroup 1
2nd rowGroup 1
3rd rowGroup 4
4th rowGroup 4
5th rowGroup 2
ValueCountFrequency (%)
group 1736
36.7%
not 632
 
13.3%
applicable 632
 
13.3%
b 228
 
4.8%
a 228
 
4.8%
c 204
 
4.3%
d 180
 
3.8%
e 132
 
2.8%
f 132
 
2.8%
1 124
 
2.6%
Other values (7) 508
 
10.7%
2023-10-23T23:40:26.704760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 3000
14.3%
o 2368
11.3%
2368
11.3%
G 1808
8.6%
u 1736
8.3%
r 1736
8.3%
a 1264
 
6.0%
l 1264
 
6.0%
c 634
 
3.0%
e 632
 
3.0%
Other values (17) 4190
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15162
72.2%
Uppercase Letter 2982
 
14.2%
Space Separator 2368
 
11.3%
Decimal Number 488
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 3000
19.8%
o 2368
15.6%
u 1736
11.4%
r 1736
11.4%
a 1264
8.3%
l 1264
8.3%
c 634
 
4.2%
e 632
 
4.2%
n 632
 
4.2%
b 632
 
4.2%
Other values (2) 1264
8.3%
Uppercase Letter
ValueCountFrequency (%)
G 1808
60.6%
B 228
 
7.6%
A 228
 
7.6%
C 202
 
6.8%
D 180
 
6.0%
F 132
 
4.4%
E 132
 
4.4%
H 72
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 124
25.4%
2 118
24.2%
3 112
23.0%
4 110
22.5%
6 12
 
2.5%
5 12
 
2.5%
Space Separator
ValueCountFrequency (%)
2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18144
86.4%
Common 2856
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 3000
16.5%
o 2368
13.1%
G 1808
10.0%
u 1736
9.6%
r 1736
9.6%
a 1264
7.0%
l 1264
7.0%
c 634
 
3.5%
e 632
 
3.5%
n 632
 
3.5%
Other values (10) 3070
16.9%
Common
ValueCountFrequency (%)
2368
82.9%
1 124
 
4.3%
2 118
 
4.1%
3 112
 
3.9%
4 110
 
3.9%
6 12
 
0.4%
5 12
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 3000
14.3%
o 2368
11.3%
2368
11.3%
G 1808
8.6%
u 1736
8.3%
r 1736
8.3%
a 1264
 
6.0%
l 1264
 
6.0%
c 634
 
3.0%
e 632
 
3.0%
Other values (17) 4190
20.0%

group_stage
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7381756757
Minimum0
Maximum1
Zeros620
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:26.897072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4397203661
Coefficient of variation (CV)0.5956852557
Kurtosis-0.8251626616
Mean0.7381756757
Median Absolute Deviation (MAD)0
Skewness-1.084220909
Sum1748
Variance0.1933540004
MonotonicityNot monotonic
2023-10-23T23:40:27.062192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 1748
73.8%
0 620
 
26.2%
ValueCountFrequency (%)
0 620
 
26.2%
1 1748
73.8%
ValueCountFrequency (%)
1 1748
73.8%
0 620
 
26.2%

knockout_stage
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2618243243
Minimum0
Maximum1
Zeros1748
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:27.421747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4397203661
Coefficient of variation (CV)1.679448108
Kurtosis-0.8251626616
Mean0.2618243243
Median Absolute Deviation (MAD)0
Skewness1.084220909
Sum620
Variance0.1933540004
MonotonicityNot monotonic
2023-10-23T23:40:27.724827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1748
73.8%
1 620
 
26.2%
ValueCountFrequency (%)
0 1748
73.8%
1 620
 
26.2%
ValueCountFrequency (%)
1 620
 
26.2%
0 1748
73.8%

replayed
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003378378378
Minimum0
Maximum1
Zeros2360
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:28.009139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05803781008
Coefficient of variation (CV)17.17919178
Kurtosis291.6213131
Mean0.003378378378
Median Absolute Deviation (MAD)0
Skewness17.12819348
Sum8
Variance0.003368387399
MonotonicityNot monotonic
2023-10-23T23:40:28.293912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 2360
99.7%
1 8
 
0.3%
ValueCountFrequency (%)
0 2360
99.7%
1 8
 
0.3%
ValueCountFrequency (%)
1 8
 
0.3%
0 2360
99.7%

replay
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003378378378
Minimum0
Maximum1
Zeros2360
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:28.513749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05803781008
Coefficient of variation (CV)17.17919178
Kurtosis291.6213131
Mean0.003378378378
Median Absolute Deviation (MAD)0
Skewness17.12819348
Sum8
Variance0.003368387399
MonotonicityNot monotonic
2023-10-23T23:40:28.713557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 2360
99.7%
1 8
 
0.3%
ValueCountFrequency (%)
0 2360
99.7%
1 8
 
0.3%
ValueCountFrequency (%)
1 8
 
0.3%
0 2360
99.7%

match_date
Text

Distinct467
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:29.013092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters23680
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1930-07-13
2nd row1930-07-13
3rd row1930-07-13
4th row1930-07-13
5th row1930-07-14
ValueCountFrequency (%)
1934-05-27 16
 
0.7%
1958-06-15 16
 
0.7%
1958-06-08 16
 
0.7%
1958-06-11 14
 
0.6%
1938-06-05 12
 
0.5%
1991-11-19 12
 
0.5%
1991-11-21 12
 
0.5%
1991-11-17 10
 
0.4%
1950-07-02 10
 
0.4%
1954-06-16 8
 
0.3%
Other values (457) 2242
94.7%
2023-10-23T23:40:29.592933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4912
20.7%
- 4736
20.0%
1 3438
14.5%
6 2436
10.3%
2 2344
9.9%
9 2324
9.8%
7 902
 
3.8%
8 846
 
3.6%
5 614
 
2.6%
4 612
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18944
80.0%
Dash Punctuation 4736
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4912
25.9%
1 3438
18.1%
6 2436
12.9%
2 2344
12.4%
9 2324
12.3%
7 902
 
4.8%
8 846
 
4.5%
5 614
 
3.2%
4 612
 
3.2%
3 516
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 4736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23680
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4912
20.7%
- 4736
20.0%
1 3438
14.5%
6 2436
10.3%
2 2344
9.9%
9 2324
9.8%
7 902
 
3.8%
8 846
 
3.6%
5 614
 
2.6%
4 612
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4912
20.7%
- 4736
20.0%
1 3438
14.5%
6 2436
10.3%
2 2344
9.9%
9 2324
9.8%
7 902
 
3.8%
8 846
 
3.6%
5 614
 
2.6%
4 612
 
2.6%

match_time
Text

Distinct46
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:29.862274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15:00
2nd row15:00
3rd row15:00
4th row15:00
5th row12:45
ValueCountFrequency (%)
21:00 312
13.2%
16:00 308
13.0%
17:00 204
 
8.6%
15:00 190
 
8.0%
18:00 174
 
7.3%
19:00 132
 
5.6%
20:30 122
 
5.2%
19:30 104
 
4.4%
12:00 104
 
4.4%
20:00 74
 
3.1%
Other values (36) 644
27.2%
2023-10-23T23:40:30.290000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3952
33.4%
: 2368
20.0%
1 2252
19.0%
2 686
 
5.8%
3 636
 
5.4%
5 516
 
4.4%
6 402
 
3.4%
7 314
 
2.7%
9 286
 
2.4%
4 220
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9472
80.0%
Other Punctuation 2368
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3952
41.7%
1 2252
23.8%
2 686
 
7.2%
3 636
 
6.7%
5 516
 
5.4%
6 402
 
4.2%
7 314
 
3.3%
9 286
 
3.0%
4 220
 
2.3%
8 208
 
2.2%
Other Punctuation
ValueCountFrequency (%)
: 2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3952
33.4%
: 2368
20.0%
1 2252
19.0%
2 686
 
5.8%
3 636
 
5.4%
5 516
 
4.4%
6 402
 
3.4%
7 314
 
2.7%
9 286
 
2.4%
4 220
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3952
33.4%
: 2368
20.0%
1 2252
19.0%
2 686
 
5.8%
3 636
 
5.4%
5 516
 
4.4%
6 402
 
3.4%
7 314
 
2.7%
9 286
 
2.4%
4 220
 
1.9%

stadium_id
Text

Distinct232
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:30.743313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11840
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS-240
2nd rowS-240
3rd rowS-239
4th rowS-239
5th rowS-239
ValueCountFrequency (%)
s-132 38
 
1.6%
s-068 32
 
1.4%
s-020 30
 
1.3%
s-086 28
 
1.2%
s-128 28
 
1.2%
s-237 22
 
0.9%
s-131 22
 
0.9%
s-065 22
 
0.9%
s-024 22
 
0.9%
s-032 20
 
0.8%
Other values (222) 2104
88.9%
2023-10-23T23:40:31.315260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 2368
20.0%
- 2368
20.0%
0 1662
14.0%
1 1276
10.8%
2 1066
9.0%
3 588
 
5.0%
6 490
 
4.1%
8 480
 
4.1%
9 412
 
3.5%
5 388
 
3.3%
Other values (2) 742
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7104
60.0%
Uppercase Letter 2368
 
20.0%
Dash Punctuation 2368
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1662
23.4%
1 1276
18.0%
2 1066
15.0%
3 588
 
8.3%
6 490
 
6.9%
8 480
 
6.8%
9 412
 
5.8%
5 388
 
5.5%
7 384
 
5.4%
4 358
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 2368
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9472
80.0%
Latin 2368
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2368
25.0%
0 1662
17.5%
1 1276
13.5%
2 1066
11.3%
3 588
 
6.2%
6 490
 
5.2%
8 480
 
5.1%
9 412
 
4.3%
5 388
 
4.1%
7 384
 
4.1%
Latin
ValueCountFrequency (%)
S 2368
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 2368
20.0%
- 2368
20.0%
0 1662
14.0%
1 1276
10.8%
2 1066
9.0%
3 588
 
5.0%
6 490
 
4.1%
8 480
 
4.1%
9 412
 
3.5%
5 388
 
3.3%
Other values (2) 742
 
6.3%

stadium_name
Text

Distinct227
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:31.586035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length30
Median length25
Mean length16.88766892
Min length6

Characters and Unicode

Total characters39990
Distinct characters68
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstadio Pocitos
2nd rowEstadio Pocitos
3rd rowEstadio Gran Parque Central
4th rowEstadio Gran Parque Central
5th rowEstadio Gran Parque Central
ValueCountFrequency (%)
stadium 656
 
11.6%
estadio 406
 
7.2%
stade 212
 
3.8%
arena 164
 
2.9%
de 130
 
2.3%
stadio 120
 
2.1%
estádio 106
 
1.9%
la 86
 
1.5%
park 70
 
1.2%
center 62
 
1.1%
Other values (308) 3636
64.4%
2023-10-23T23:40:32.109446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4408
 
11.0%
3280
 
8.2%
i 2966
 
7.4%
o 2818
 
7.0%
t 2686
 
6.7%
d 2502
 
6.3%
e 2458
 
6.1%
n 2000
 
5.0%
r 1696
 
4.2%
s 1618
 
4.0%
Other values (58) 13558
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30900
77.3%
Uppercase Letter 5640
 
14.1%
Space Separator 3280
 
8.2%
Dash Punctuation 104
 
0.3%
Other Punctuation 54
 
0.1%
Decimal Number 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4408
14.3%
i 2966
9.6%
o 2818
9.1%
t 2686
8.7%
d 2502
8.1%
e 2458
 
8.0%
n 2000
 
6.5%
r 1696
 
5.5%
s 1618
 
5.2%
l 1536
 
5.0%
Other values (26) 6212
20.1%
Uppercase Letter
ValueCountFrequency (%)
S 1326
23.5%
E 596
10.6%
C 500
 
8.9%
A 368
 
6.5%
P 360
 
6.4%
M 260
 
4.6%
F 228
 
4.0%
R 222
 
3.9%
N 222
 
3.9%
B 206
 
3.7%
Other values (16) 1352
24.0%
Other Punctuation
ValueCountFrequency (%)
. 28
51.9%
' 26
48.1%
Decimal Number
ValueCountFrequency (%)
8 6
50.0%
6 6
50.0%
Space Separator
ValueCountFrequency (%)
3280
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36540
91.4%
Common 3450
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4408
 
12.1%
i 2966
 
8.1%
o 2818
 
7.7%
t 2686
 
7.4%
d 2502
 
6.8%
e 2458
 
6.7%
n 2000
 
5.5%
r 1696
 
4.6%
s 1618
 
4.4%
l 1536
 
4.2%
Other values (52) 11852
32.4%
Common
ValueCountFrequency (%)
3280
95.1%
- 104
 
3.0%
. 28
 
0.8%
' 26
 
0.8%
8 6
 
0.2%
6 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39584
99.0%
None 406
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4408
 
11.1%
3280
 
8.3%
i 2966
 
7.5%
o 2818
 
7.1%
t 2686
 
6.8%
d 2502
 
6.3%
e 2458
 
6.2%
n 2000
 
5.1%
r 1696
 
4.3%
s 1618
 
4.1%
Other values (46) 13152
33.2%
None
ValueCountFrequency (%)
é 114
28.1%
á 114
28.1%
ã 54
13.3%
í 30
 
7.4%
ó 28
 
6.9%
å 18
 
4.4%
ö 18
 
4.4%
ô 8
 
2.0%
ê 6
 
1.5%
à 6
 
1.5%
Other values (2) 10
 
2.5%

city_name
Text

Distinct197
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:32.437212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.300675676
Min length4

Characters and Unicode

Total characters19656
Distinct characters67
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMontevideo
2nd rowMontevideo
3rd rowMontevideo
4th rowMontevideo
5th rowMontevideo
ValueCountFrequency (%)
city 54
 
1.9%
mexico 46
 
1.7%
paris 38
 
1.4%
montevideo 36
 
1.3%
guadalajara 34
 
1.2%
washington 34
 
1.2%
d.c 34
 
1.2%
rio 30
 
1.1%
de 30
 
1.1%
janeiro 30
 
1.1%
Other values (207) 2406
86.8%
2023-10-23T23:40:33.008750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1990
 
10.1%
o 1692
 
8.6%
e 1672
 
8.5%
n 1634
 
8.3%
r 1282
 
6.5%
i 1162
 
5.9%
l 876
 
4.5%
t 848
 
4.3%
u 762
 
3.9%
s 748
 
3.8%
Other values (57) 6990
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16312
83.0%
Uppercase Letter 2788
 
14.2%
Space Separator 404
 
2.1%
Other Punctuation 102
 
0.5%
Dash Punctuation 50
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1990
12.2%
o 1692
10.4%
e 1672
10.3%
n 1634
10.0%
r 1282
 
7.9%
i 1162
 
7.1%
l 876
 
5.4%
t 848
 
5.2%
u 762
 
4.7%
s 748
 
4.6%
Other values (25) 3646
22.4%
Uppercase Letter
ValueCountFrequency (%)
M 334
 
12.0%
S 284
 
10.2%
C 214
 
7.7%
P 212
 
7.6%
B 206
 
7.4%
R 150
 
5.4%
G 142
 
5.1%
D 140
 
5.0%
L 138
 
4.9%
N 114
 
4.1%
Other values (18) 854
30.6%
Other Punctuation
ValueCountFrequency (%)
. 68
66.7%
, 34
33.3%
Space Separator
ValueCountFrequency (%)
404
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19100
97.2%
Common 556
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1990
 
10.4%
o 1692
 
8.9%
e 1672
 
8.8%
n 1634
 
8.6%
r 1282
 
6.7%
i 1162
 
6.1%
l 876
 
4.6%
t 848
 
4.4%
u 762
 
4.0%
s 748
 
3.9%
Other values (53) 6434
33.7%
Common
ValueCountFrequency (%)
404
72.7%
. 68
 
12.2%
- 50
 
9.0%
, 34
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19418
98.8%
None 238
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1990
 
10.2%
o 1692
 
8.7%
e 1672
 
8.6%
n 1634
 
8.4%
r 1282
 
6.6%
i 1162
 
6.0%
l 876
 
4.5%
t 848
 
4.4%
u 762
 
3.9%
s 748
 
3.9%
Other values (44) 6752
34.8%
None
ValueCountFrequency (%)
ó 50
21.0%
ä 26
10.9%
ã 24
10.1%
ñ 22
9.2%
ü 20
 
8.4%
å 20
 
8.4%
ö 20
 
8.4%
á 14
 
5.9%
í 14
 
5.9%
É 12
 
5.0%
Other values (3) 16
 
6.7%

country_name
Text

Distinct19
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:33.237681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.30152027
Min length5

Characters and Unicode

Total characters17290
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUruguay
2nd rowUruguay
3rd rowUruguay
4th rowUruguay
5th rowUruguay
ValueCountFrequency (%)
germany 268
 
9.6%
france 268
 
9.6%
united 232
 
8.3%
states 232
 
8.3%
south 192
 
6.9%
brazil 172
 
6.2%
mexico 168
 
6.0%
italy 138
 
4.9%
africa 128
 
4.6%
russia 128
 
4.6%
Other values (11) 866
31.0%
2023-10-23T23:40:33.741107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2286
 
13.2%
e 1668
 
9.6%
n 1610
 
9.3%
i 1240
 
7.2%
t 1154
 
6.7%
r 1064
 
6.2%
S 702
 
4.1%
d 574
 
3.3%
c 564
 
3.3%
l 490
 
2.8%
Other values (25) 5938
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14074
81.4%
Uppercase Letter 2792
 
16.1%
Space Separator 424
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2286
16.2%
e 1668
11.9%
n 1610
11.4%
i 1240
8.8%
t 1154
 
8.2%
r 1064
 
7.6%
d 574
 
4.1%
c 564
 
4.0%
l 490
 
3.5%
s 488
 
3.5%
Other values (11) 2936
20.9%
Uppercase Letter
ValueCountFrequency (%)
S 702
25.1%
C 284
10.2%
U 268
 
9.6%
F 268
 
9.6%
G 268
 
9.6%
A 204
 
7.3%
B 172
 
6.2%
M 168
 
6.0%
I 138
 
4.9%
R 128
 
4.6%
Other values (3) 192
 
6.9%
Space Separator
ValueCountFrequency (%)
424
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16866
97.5%
Common 424
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2286
13.6%
e 1668
 
9.9%
n 1610
 
9.5%
i 1240
 
7.4%
t 1154
 
6.8%
r 1064
 
6.3%
S 702
 
4.2%
d 574
 
3.4%
c 564
 
3.3%
l 490
 
2.9%
Other values (24) 5514
32.7%
Common
ValueCountFrequency (%)
424
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2286
 
13.2%
e 1668
 
9.6%
n 1610
 
9.3%
i 1240
 
7.2%
t 1154
 
6.7%
r 1064
 
6.2%
S 702
 
4.1%
d 574
 
3.3%
c 564
 
3.3%
l 490
 
2.8%
Other values (25) 5938
34.3%

team_id
Text

Distinct87
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:34.059818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters9472
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowT-30
2nd rowT-46
3rd rowT-83
4th rowT-06
5th rowT-87
ValueCountFrequency (%)
t-09 143
 
6.0%
t-41 95
 
4.0%
t-28 95
 
4.0%
t-74 91
 
3.8%
t-31 91
 
3.8%
t-03 90
 
3.8%
t-30 85
 
3.6%
t-83 83
 
3.5%
t-73 70
 
3.0%
t-46 66
 
2.8%
Other values (77) 1459
61.6%
2023-10-23T23:40:34.587307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2368
25.0%
- 2368
25.0%
4 654
 
6.9%
3 632
 
6.7%
1 589
 
6.2%
0 576
 
6.1%
8 501
 
5.3%
7 445
 
4.7%
6 399
 
4.2%
5 379
 
4.0%
Other values (2) 561
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4736
50.0%
Uppercase Letter 2368
25.0%
Dash Punctuation 2368
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 654
13.8%
3 632
13.3%
1 589
12.4%
0 576
12.2%
8 501
10.6%
7 445
9.4%
6 399
8.4%
5 379
8.0%
2 367
7.7%
9 194
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
T 2368
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7104
75.0%
Latin 2368
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2368
33.3%
4 654
 
9.2%
3 632
 
8.9%
1 589
 
8.3%
0 576
 
8.1%
8 501
 
7.1%
7 445
 
6.3%
6 399
 
5.6%
5 379
 
5.3%
2 367
 
5.2%
Latin
ValueCountFrequency (%)
T 2368
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2368
25.0%
- 2368
25.0%
4 654
 
6.9%
3 632
 
6.7%
1 589
 
6.2%
0 576
 
6.1%
8 501
 
5.3%
7 445
 
4.7%
6 399
 
4.2%
5 379
 
4.0%
Other values (2) 561
 
5.9%

team_name
Text

Distinct87
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:34.925873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.834459459
Min length4

Characters and Unicode

Total characters18552
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFrance
2nd rowMexico
3rd rowUnited States
4th rowBelgium
5th rowYugoslavia
ValueCountFrequency (%)
germany 159
 
5.7%
brazil 143
 
5.1%
italy 95
 
3.4%
england 95
 
3.4%
sweden 91
 
3.3%
argentina 90
 
3.2%
united 86
 
3.1%
france 85
 
3.1%
states 83
 
3.0%
spain 70
 
2.5%
Other values (94) 1780
64.1%
2023-10-23T23:40:35.628102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2535
 
13.7%
e 1549
 
8.3%
n 1509
 
8.1%
r 1331
 
7.2%
i 1249
 
6.7%
t 934
 
5.0%
l 908
 
4.9%
o 860
 
4.6%
d 580
 
3.1%
u 571
 
3.1%
Other values (37) 6526
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15388
82.9%
Uppercase Letter 2755
 
14.9%
Space Separator 409
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2535
16.5%
e 1549
10.1%
n 1509
9.8%
r 1331
 
8.6%
i 1249
 
8.1%
t 934
 
6.1%
l 908
 
5.9%
o 860
 
5.6%
d 580
 
3.8%
u 571
 
3.7%
Other values (15) 3362
21.8%
Uppercase Letter
ValueCountFrequency (%)
S 447
16.2%
C 258
9.4%
B 226
 
8.2%
N 210
 
7.6%
A 208
 
7.5%
G 193
 
7.0%
U 178
 
6.5%
I 158
 
5.7%
E 134
 
4.9%
P 112
 
4.1%
Other values (11) 631
22.9%
Space Separator
ValueCountFrequency (%)
409
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18143
97.8%
Common 409
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2535
14.0%
e 1549
 
8.5%
n 1509
 
8.3%
r 1331
 
7.3%
i 1249
 
6.9%
t 934
 
5.1%
l 908
 
5.0%
o 860
 
4.7%
d 580
 
3.2%
u 571
 
3.1%
Other values (36) 6117
33.7%
Common
ValueCountFrequency (%)
409
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2535
 
13.7%
e 1549
 
8.3%
n 1509
 
8.1%
r 1331
 
7.2%
i 1249
 
6.7%
t 934
 
5.0%
l 908
 
4.9%
o 860
 
4.6%
d 580
 
3.1%
u 571
 
3.1%
Other values (37) 6526
35.2%

team_code
Text

Distinct86
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:35.992439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7104
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFRA
2nd rowMEX
3rd rowUSA
4th rowBEL
5th rowYUG
ValueCountFrequency (%)
deu 153
 
6.5%
bra 143
 
6.0%
eng 95
 
4.0%
ita 95
 
4.0%
swe 91
 
3.8%
arg 90
 
3.8%
fra 85
 
3.6%
usa 83
 
3.5%
esp 70
 
3.0%
mex 66
 
2.8%
Other values (76) 1397
59.0%
2023-10-23T23:40:36.580642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 808
 
11.4%
A 748
 
10.5%
E 616
 
8.7%
U 568
 
8.0%
N 566
 
8.0%
S 450
 
6.3%
G 345
 
4.9%
C 327
 
4.6%
D 286
 
4.0%
L 262
 
3.7%
Other values (16) 2128
30.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7104
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 808
 
11.4%
A 748
 
10.5%
E 616
 
8.7%
U 568
 
8.0%
N 566
 
8.0%
S 450
 
6.3%
G 345
 
4.9%
C 327
 
4.6%
D 286
 
4.0%
L 262
 
3.7%
Other values (16) 2128
30.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7104
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 808
 
11.4%
A 748
 
10.5%
E 616
 
8.7%
U 568
 
8.0%
N 566
 
8.0%
S 450
 
6.3%
G 345
 
4.9%
C 327
 
4.6%
D 286
 
4.0%
L 262
 
3.7%
Other values (16) 2128
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 808
 
11.4%
A 748
 
10.5%
E 616
 
8.7%
U 568
 
8.0%
N 566
 
8.0%
S 450
 
6.3%
G 345
 
4.9%
C 327
 
4.6%
D 286
 
4.0%
L 262
 
3.7%
Other values (16) 2128
30.0%

opponent_id
Text

Distinct87
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:36.913419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters9472
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowT-46
2nd rowT-30
3rd rowT-06
4th rowT-83
5th rowT-09
ValueCountFrequency (%)
t-09 143
 
6.0%
t-41 95
 
4.0%
t-28 95
 
4.0%
t-31 91
 
3.8%
t-74 91
 
3.8%
t-03 90
 
3.8%
t-30 85
 
3.6%
t-83 83
 
3.5%
t-73 70
 
3.0%
t-46 66
 
2.8%
Other values (77) 1459
61.6%
2023-10-23T23:40:37.417945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2368
25.0%
- 2368
25.0%
4 654
 
6.9%
3 632
 
6.7%
1 589
 
6.2%
0 576
 
6.1%
8 501
 
5.3%
7 445
 
4.7%
6 399
 
4.2%
5 379
 
4.0%
Other values (2) 561
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4736
50.0%
Uppercase Letter 2368
25.0%
Dash Punctuation 2368
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 654
13.8%
3 632
13.3%
1 589
12.4%
0 576
12.2%
8 501
10.6%
7 445
9.4%
6 399
8.4%
5 379
8.0%
2 367
7.7%
9 194
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
T 2368
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7104
75.0%
Latin 2368
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2368
33.3%
4 654
 
9.2%
3 632
 
8.9%
1 589
 
8.3%
0 576
 
8.1%
8 501
 
7.1%
7 445
 
6.3%
6 399
 
5.6%
5 379
 
5.3%
2 367
 
5.2%
Latin
ValueCountFrequency (%)
T 2368
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2368
25.0%
- 2368
25.0%
4 654
 
6.9%
3 632
 
6.7%
1 589
 
6.2%
0 576
 
6.1%
8 501
 
5.3%
7 445
 
4.7%
6 399
 
4.2%
5 379
 
4.0%
Other values (2) 561
 
5.9%

opponent_name
Text

Distinct87
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:37.805512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.834459459
Min length4

Characters and Unicode

Total characters18552
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMexico
2nd rowFrance
3rd rowBelgium
4th rowUnited States
5th rowBrazil
ValueCountFrequency (%)
germany 159
 
5.7%
brazil 143
 
5.1%
italy 95
 
3.4%
england 95
 
3.4%
sweden 91
 
3.3%
argentina 90
 
3.2%
united 86
 
3.1%
france 85
 
3.1%
states 83
 
3.0%
spain 70
 
2.5%
Other values (94) 1780
64.1%
2023-10-23T23:40:38.337836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2535
 
13.7%
e 1549
 
8.3%
n 1509
 
8.1%
r 1331
 
7.2%
i 1249
 
6.7%
t 934
 
5.0%
l 908
 
4.9%
o 860
 
4.6%
d 580
 
3.1%
u 571
 
3.1%
Other values (37) 6526
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15388
82.9%
Uppercase Letter 2755
 
14.9%
Space Separator 409
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2535
16.5%
e 1549
10.1%
n 1509
9.8%
r 1331
 
8.6%
i 1249
 
8.1%
t 934
 
6.1%
l 908
 
5.9%
o 860
 
5.6%
d 580
 
3.8%
u 571
 
3.7%
Other values (15) 3362
21.8%
Uppercase Letter
ValueCountFrequency (%)
S 447
16.2%
C 258
9.4%
B 226
 
8.2%
N 210
 
7.6%
A 208
 
7.5%
G 193
 
7.0%
U 178
 
6.5%
I 158
 
5.7%
E 134
 
4.9%
P 112
 
4.1%
Other values (11) 631
22.9%
Space Separator
ValueCountFrequency (%)
409
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18143
97.8%
Common 409
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2535
14.0%
e 1549
 
8.5%
n 1509
 
8.3%
r 1331
 
7.3%
i 1249
 
6.9%
t 934
 
5.1%
l 908
 
5.0%
o 860
 
4.7%
d 580
 
3.2%
u 571
 
3.1%
Other values (36) 6117
33.7%
Common
ValueCountFrequency (%)
409
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2535
 
13.7%
e 1549
 
8.3%
n 1509
 
8.1%
r 1331
 
7.2%
i 1249
 
6.7%
t 934
 
5.0%
l 908
 
4.9%
o 860
 
4.6%
d 580
 
3.1%
u 571
 
3.1%
Other values (37) 6526
35.2%

opponent_code
Text

Distinct86
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:38.624876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7104
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMEX
2nd rowFRA
3rd rowBEL
4th rowUSA
5th rowBRA
ValueCountFrequency (%)
deu 153
 
6.5%
bra 143
 
6.0%
eng 95
 
4.0%
ita 95
 
4.0%
swe 91
 
3.8%
arg 90
 
3.8%
fra 85
 
3.6%
usa 83
 
3.5%
esp 70
 
3.0%
mex 66
 
2.8%
Other values (76) 1397
59.0%
2023-10-23T23:40:39.068666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 808
 
11.4%
A 748
 
10.5%
E 616
 
8.7%
U 568
 
8.0%
N 566
 
8.0%
S 450
 
6.3%
G 345
 
4.9%
C 327
 
4.6%
D 286
 
4.0%
L 262
 
3.7%
Other values (16) 2128
30.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7104
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 808
 
11.4%
A 748
 
10.5%
E 616
 
8.7%
U 568
 
8.0%
N 566
 
8.0%
S 450
 
6.3%
G 345
 
4.9%
C 327
 
4.6%
D 286
 
4.0%
L 262
 
3.7%
Other values (16) 2128
30.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7104
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 808
 
11.4%
A 748
 
10.5%
E 616
 
8.7%
U 568
 
8.0%
N 566
 
8.0%
S 450
 
6.3%
G 345
 
4.9%
C 327
 
4.6%
D 286
 
4.0%
L 262
 
3.7%
Other values (16) 2128
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 808
 
11.4%
A 748
 
10.5%
E 616
 
8.7%
U 568
 
8.0%
N 566
 
8.0%
S 450
 
6.3%
G 345
 
4.9%
C 327
 
4.6%
D 286
 
4.0%
L 262
 
3.7%
Other values (16) 2128
30.0%

home_team
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum0
Maximum1
Zeros1184
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:39.261981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5001056078
Coefficient of variation (CV)1.000211216
Kurtosis-2.001691332
Mean0.5
Median Absolute Deviation (MAD)0.5
Skewness0
Sum1184
Variance0.2501056189
MonotonicityNot monotonic
2023-10-23T23:40:39.449563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 1184
50.0%
0 1184
50.0%
ValueCountFrequency (%)
0 1184
50.0%
1 1184
50.0%
ValueCountFrequency (%)
1 1184
50.0%
0 1184
50.0%

away_team
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum0
Maximum1
Zeros1184
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:39.594383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5001056078
Coefficient of variation (CV)1.000211216
Kurtosis-2.001691332
Mean0.5
Median Absolute Deviation (MAD)0.5
Skewness0
Sum1184
Variance0.2501056189
MonotonicityNot monotonic
2023-10-23T23:40:39.781595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1184
50.0%
1 1184
50.0%
ValueCountFrequency (%)
0 1184
50.0%
1 1184
50.0%
ValueCountFrequency (%)
1 1184
50.0%
0 1184
50.0%

goals_for
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.463260135
Minimum0
Maximum13
Zeros677
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:39.934237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.503805008
Coefficient of variation (CV)1.027708588
Kurtosis5.451217836
Mean1.463260135
Median Absolute Deviation (MAD)1
Skewness1.788729955
Sum3465
Variance2.261429502
MonotonicityNot monotonic
2023-10-23T23:40:40.124357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 764
32.3%
0 677
28.6%
2 488
20.6%
3 242
 
10.2%
4 103
 
4.3%
5 38
 
1.6%
6 24
 
1.0%
7 19
 
0.8%
8 6
 
0.3%
10 3
 
0.1%
Other values (3) 4
 
0.2%
ValueCountFrequency (%)
0 677
28.6%
1 764
32.3%
2 488
20.6%
3 242
 
10.2%
4 103
 
4.3%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 1
 
< 0.1%
10 3
0.1%
9 2
 
0.1%
8 6
0.3%

goals_against
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.463260135
Minimum0
Maximum13
Zeros677
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:40.301200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.503805008
Coefficient of variation (CV)1.027708588
Kurtosis5.451217836
Mean1.463260135
Median Absolute Deviation (MAD)1
Skewness1.788729955
Sum3465
Variance2.261429502
MonotonicityNot monotonic
2023-10-23T23:40:40.492251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 764
32.3%
0 677
28.6%
2 488
20.6%
3 242
 
10.2%
4 103
 
4.3%
5 38
 
1.6%
6 24
 
1.0%
7 19
 
0.8%
8 6
 
0.3%
10 3
 
0.1%
Other values (3) 4
 
0.2%
ValueCountFrequency (%)
0 677
28.6%
1 764
32.3%
2 488
20.6%
3 242
 
10.2%
4 103
 
4.3%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 1
 
< 0.1%
10 3
0.1%
9 2
 
0.1%
8 6
0.3%

goal_differential
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-13
Maximum13
Zeros476
Zeros (%)20.1%
Negative946
Negative (%)39.9%
Memory size9.4 KiB
2023-10-23T23:40:40.679601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-13
5-th percentile-3
Q1-1
median0
Q31
95-th percentile3
Maximum13
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.27382007
Coefficient of variation (CV)nan
Kurtosis2.84924322
Mean0
Median Absolute Deviation (MAD)1
Skewness0
Sum0
Variance5.17025771
MonotonicityNot monotonic
2023-10-23T23:40:40.878841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 476
20.1%
1 462
19.5%
-1 462
19.5%
2 237
10.0%
-2 237
10.0%
-3 132
 
5.6%
3 132
 
5.6%
-4 48
 
2.0%
4 48
 
2.0%
-5 32
 
1.4%
Other values (15) 102
 
4.3%
ValueCountFrequency (%)
-13 1
 
< 0.1%
-11 1
 
< 0.1%
-10 1
 
< 0.1%
-9 4
0.2%
-8 5
0.2%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 4
0.2%
8 5
0.2%

extra_time
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07010135135
Minimum0
Maximum1
Zeros2202
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:41.065705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.255371674
Coefficient of variation (CV)3.642892313
Kurtosis9.362736575
Mean0.07010135135
Median Absolute Deviation (MAD)0
Skewness3.3696928
Sum166
Variance0.06521469188
MonotonicityNot monotonic
2023-10-23T23:40:41.220681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 2202
93.0%
1 166
 
7.0%
ValueCountFrequency (%)
0 2202
93.0%
1 166
 
7.0%
ValueCountFrequency (%)
1 166
 
7.0%
0 2202
93.0%

penalty_shootout
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03209459459
Minimum0
Maximum1
Zeros2292
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:41.379124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1762885578
Coefficient of variation (CV)5.492780326
Kurtosis26.24897787
Mean0.03209459459
Median Absolute Deviation (MAD)0
Skewness5.312890817
Sum76
Variance0.0310776556
MonotonicityNot monotonic
2023-10-23T23:40:41.561203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 2292
96.8%
1 76
 
3.2%
ValueCountFrequency (%)
0 2292
96.8%
1 76
 
3.2%
ValueCountFrequency (%)
1 76
 
3.2%
0 2292
96.8%

penalties_for
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1072635135
Minimum0
Maximum5
Zeros2293
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:41.707731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6243729656
Coefficient of variation (CV)5.820925915
Kurtosis36.73054274
Mean0.1072635135
Median Absolute Deviation (MAD)0
Skewness6.059416484
Sum254
Variance0.3898416002
MonotonicityNot monotonic
2023-10-23T23:40:41.909962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2293
96.8%
3 25
 
1.1%
4 24
 
1.0%
5 12
 
0.5%
2 9
 
0.4%
1 5
 
0.2%
ValueCountFrequency (%)
0 2293
96.8%
1 5
 
0.2%
2 9
 
0.4%
3 25
 
1.1%
4 24
 
1.0%
ValueCountFrequency (%)
5 12
0.5%
4 24
1.0%
3 25
1.1%
2 9
 
0.4%
1 5
 
0.2%

penalties_against
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1072635135
Minimum0
Maximum5
Zeros2293
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:42.077002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6243729656
Coefficient of variation (CV)5.820925915
Kurtosis36.73054274
Mean0.1072635135
Median Absolute Deviation (MAD)0
Skewness6.059416484
Sum254
Variance0.3898416002
MonotonicityNot monotonic
2023-10-23T23:40:42.400671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2293
96.8%
3 25
 
1.1%
4 24
 
1.0%
5 12
 
0.5%
2 9
 
0.4%
1 5
 
0.2%
ValueCountFrequency (%)
0 2293
96.8%
1 5
 
0.2%
2 9
 
0.4%
3 25
 
1.1%
4 24
 
1.0%
ValueCountFrequency (%)
5 12
0.5%
4 24
1.0%
3 25
1.1%
2 9
 
0.4%
1 5
 
0.2%

result
Text

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2023-10-23T23:40:42.536906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.584459459
Min length3

Characters and Unicode

Total characters8488
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwin
2nd rowlose
3rd rowwin
4th rowlose
5th rowwin
ValueCountFrequency (%)
win 984
41.6%
lose 984
41.6%
draw 400
16.9%
2023-10-23T23:40:42.891427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 1384
16.3%
i 984
11.6%
n 984
11.6%
l 984
11.6%
o 984
11.6%
s 984
11.6%
e 984
11.6%
d 400
 
4.7%
r 400
 
4.7%
a 400
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8488
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 1384
16.3%
i 984
11.6%
n 984
11.6%
l 984
11.6%
o 984
11.6%
s 984
11.6%
e 984
11.6%
d 400
 
4.7%
r 400
 
4.7%
a 400
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 8488
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 1384
16.3%
i 984
11.6%
n 984
11.6%
l 984
11.6%
o 984
11.6%
s 984
11.6%
e 984
11.6%
d 400
 
4.7%
r 400
 
4.7%
a 400
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 1384
16.3%
i 984
11.6%
n 984
11.6%
l 984
11.6%
o 984
11.6%
s 984
11.6%
e 984
11.6%
d 400
 
4.7%
r 400
 
4.7%
a 400
 
4.7%

win
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4155405405
Minimum0
Maximum1
Zeros1384
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:43.070360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4929190653
Coefficient of variation (CV)1.186211734
Kurtosis-1.883956159
Mean0.4155405405
Median Absolute Deviation (MAD)0
Skewness0.3429806624
Sum984
Variance0.2429692049
MonotonicityNot monotonic
2023-10-23T23:40:43.261527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1384
58.4%
1 984
41.6%
ValueCountFrequency (%)
0 1384
58.4%
1 984
41.6%
ValueCountFrequency (%)
1 984
41.6%
0 1384
58.4%

lose
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4155405405
Minimum0
Maximum1
Zeros1384
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:43.431080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4929190653
Coefficient of variation (CV)1.186211734
Kurtosis-1.883956159
Mean0.4155405405
Median Absolute Deviation (MAD)0
Skewness0.3429806624
Sum984
Variance0.2429692049
MonotonicityNot monotonic
2023-10-23T23:40:43.630664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1384
58.4%
1 984
41.6%
ValueCountFrequency (%)
0 1384
58.4%
1 984
41.6%
ValueCountFrequency (%)
1 984
41.6%
0 1384
58.4%

draw
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1689189189
Minimum0
Maximum1
Zeros1968
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-10-23T23:40:43.794535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3747594257
Coefficient of variation (CV)2.2185758
Kurtosis1.128165445
Mean0.1689189189
Median Absolute Deviation (MAD)0
Skewness1.768392862
Sum400
Variance0.1404446271
MonotonicityNot monotonic
2023-10-23T23:40:43.966183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1968
83.1%
1 400
 
16.9%
ValueCountFrequency (%)
0 1968
83.1%
1 400
 
16.9%
ValueCountFrequency (%)
1 400
 
16.9%
0 1968
83.1%